Recently deep-based models simultaneously explore the intra-saliency and inter-image consistency in a supervised manner with different approaches, such as graph convolution networks (GCN) [1]}, [2]}, self-learning methods [3]}, [4]}, correlation techniques [5]}, or co-clustering [6]}. Other works explore group-wise semantic representation which is used to detect co-salient regions for each image. There are different methods to capture the discriminative semantic representation, such as group attention semantic aggregation [7]}, gradient feedback [8]}, recurrent co-attention [9]}, [10]} and even explicit supervision of specific category labels [11]}, [12]}. However, most
of the previous methods are unstable during both training and inferring procedure when assigning different orders of the input images. And they all lack consideration of inter-image separability and cannot distinguish true common objects from noisy objects, resulting in ambiguous results when facing complex real-world scenarios. For more about CoSOD tasks, please refer to  [13]}, [14]}, [15]}.
